Last week, I was discussing about how to use `nls()`

for a specific
model with one of my colleague and I ended creating a piece of code to
show what I was talking about! Even though there are many posts exploring `nls()`

in more depth that I did (for instance this post on datascienceplus by Lionel Herzog),
I thought I could share these lines of command here!

Basically, we were talking about a model where the temperature (\(T\)) follows a saturation curve starting from 10°C at t=0 (so T(0) = 10) and plateauing at \(T_{\inf}\).

\[T(t) = T_{\inf} - (T_{\inf} - T_0)\exp(-kt)\]

The goal here is to use `nls()`

(Nonlinear Least Squares) to find \(k\) and \(T_{inf}\).
For the sack of clarity, I simulate the data, i.e. I use the saturation curve
with known parameter values, then I add some noise (here a white noise):

```
library(magrittr)
# Parameters
## known
T0 = 10
## the ones we are looking for
k = 0.2
Tinf = 20
# Simulate data
## time
seqt <- seq(0, 50, .25)
## create a data frame
simdata <- cbind(
seqt = seqt,
sim = Tinf - (Tinf- T0)*exp(-k*seqt) + .5*rnorm(length(seqt))
) %>% as.data.frame
head(simdata)
#R> seqt sim
#R> 1 0.00 10.47626
#R> 2 0.25 10.57922
#R> 3 0.50 11.57511
#R> 4 0.75 10.88814
#R> 5 1.00 11.58445
#R> 6 1.25 12.66167
```

`nls()`

Now I call `nls()`

to fit the data:

`res <- nls(sim ~ Tinf - (Tinf - 10)*exp(-k*seqt), simdata, list(Tinf = 1, k = .1))`

All the information needed are stored in `res`

and display via the print method:

```
res
#R> Nonlinear regression model
#R> model: sim ~ Tinf - (Tinf - 10) * exp(-k * seqt)
#R> data: simdata
#R> Tinf k
#R> 20.0302 0.2053
#R> residual sum-of-squares: 42.78
#R>
#R> Number of iterations to convergence: 9
#R> Achieved convergence tolerance: 5.514e-07
```

Let’s draw a quick plot:

```
## get the coefficients values
cr <- coef(res)
fitC <- function(x) cr[1] - (cr[1] - 10)*exp(-cr[2]*x)
plot(simdata[,1], simdata[,2], xlab = "Time (min)", ylab = "Temperature (°C)")
curve(fitC, 0, 50, add = TRUE, col = "#2f85a4", lwd = 3)
```